Best Local Models for OpenClaw with Ollama (2026)

Why Local Models Matter for OpenClaw

Cloud APIs cost money. Local models through Ollama are free. The most important requirement is context length — at least 64K tokens for reliable tool use.

Reddit-Style Model Shortlist

If your query is closer to “Reddit’s favorite local LLM for OpenClaw” than a benchmark table, use the community-search shortlist:

• Reddit’s favorite local LLM for OpenClaw — the practical default by hardware tier.
• Best local LLM Reddit picks for OpenClaw — the exact answer for broad “best local LLM reddit” searches.
• Best OpenClaw model Reddit users recommend — the exact OpenClaw model query with workload routing.
• Ollama local LLM Reddit picks for OpenClaw — the Ollama-first setup version.
• Best local LLM Reddit users recommend for 32GB RAM — Qwen 27B Q6 vs gpt-oss 20B Q8 for the entry serious tier.
• Best local LLM Reddit users recommend for 64GB RAM — Qwen vs gpt-oss vs Scout on the mainstream serious tier.
• Best local LLM Reddit users recommend for 128GB RAM — production 120B-class agents, Scout context, and large MoE experiments.
• Best local LLM Reddit users recommend for RTX 4090 — 24GB VRAM model picks for 4090 owners.

The short version: Qwen is still the default for most local OpenClaw users, gpt-oss is the safer production-agent path when you have enough memory, and long-context models only win when context is the actual bottleneck.

Watch: Can a Local Model Code Like Claude?

Before the comparison table, watch DeepSeek run a full live coding test against Claude. It is the fastest way to calibrate what “good enough for OpenClaw” actually looks like from a local open-weights model.

Model Comparison Table

Qwen3.5 27B is our top recommendation for most users. For long-horizon autonomous runs, see GLM-5.1 below.

GLM-5.1: The Current #1 Open-Source Model for 8-Hour Autonomous Runs

As of April 2026, Zhipu AI’s GLM-5.1 holds the #1 spot on SWE-Bench Pro among open-source models. The release got immediate signal boost from Ollama’s official account (1,673 likes) and Hugging Face’s @victormustar (1,300 likes), which tells you something: the infrastructure community, not just the model leaderboards, is paying attention.

Key specs. GLM-5.1 ships in two public sizes: a 32B dense variant for single-GPU deployment and a 355B Mixture-of-Experts variant that activates roughly 32B parameters per token. Context window is 128K natively with a 1M-token extended mode. Released by Zhipu AI (z.ai), a Beijing-based lab that has been shipping competitive open weights since the GLM-4 line. License permits commercial use with standard redistribution terms.

Where it shines. GLM-5.1 was explicitly tuned for multi-turn autonomous runs that exceed four hours. Anecdotal reports on X describe clean 8-hour agent loops with no drift on tool schemas, correct JSON argument shaping through hundreds of calls, and stable context management when paired with OpenClaw’s /compact workflow. Tool-calling accuracy on the BFCL benchmark is within 2 points of Claude 3.5 Sonnet. This is the model you pick when you are leaving an OpenClaw agent running overnight.

Hardware. The 32B dense version needs roughly 24 GB VRAM for Q4 quantization (fits on an RTX 4090, RTX A6000, or M3 Max 48GB). CPU fallback works on a machine with 48 GB unified RAM or more, though expect 2-4 tokens per second rather than the 40+ you will see on GPU. The 355B MoE variant is server-class only.

Install and configure for OpenClaw:

# Pull the 32B dense variant
ollama pull glm5.1:32b

# Set as OpenClaw's default chat model
openclaw config set agents.defaults.models.chat ollama/glm5.1:32b

# Verify
openclaw models status

# Smoke test with a tool call
openclaw chat "List the three largest files in my home directory"

One caveat. GLM-5.1 is slower to first token than Qwen3.5 27B on short interactive chats, and its English prose is slightly stiffer. If your workload is mostly quick Q&A rather than long agent runs, you are better off with Qwen. GLM-5.1 is the right pick specifically for autonomy, not conversation.

Setting Up Any Model

# 1. Pull the model
ollama pull qwen3.5:27b

# 2. Set it as your default chat model
openclaw config set agents.defaults.models.chat ollama/qwen3.5:27b

# 3. Verify
openclaw models list

# 4. Test
openclaw chat "List the files in my home directory"

Minimum Specs by Model Size

For a dedicated OpenClaw host, the Apple Mac mini M4 (16GB) handles models up to 14B comfortably.

Avoid Models Under 7B

None passed OpenClaw’s tool-calling validation consistently. Use a free-tier cloud provider instead.

For more, see our full OpenClaw troubleshooting guide.